Image scanners have come into widespread use as a means of inputting data to devices. However, if the use of image scanners is to increase further, they have to be able to scan text and pattern information with greater precision, speed and economic efficiency. To achieve this, with the exception of certain areas of application, prior art scanners store, transmit and print the scanned information after it has been binarized.
A problem is that the documents that have to be scanned are varied. In many cases the information is written on dark-colored paper, or the documents contain extraneous background information such as discolored areas as found in, for example, a copy of a document that has been copied many times. Thus, it is necessary for an image scanner not only to be able to scan a document image, but to be able to accurately extract the target textual or other information without being affected by background brightness or noise information. In prior art image scanners, the scanned information is subjected to the following processing after it has been digitized and stored in a system memory. First, the information is read out of the system memory and subjected to a prescribed series of iterative sum of products operations to obtain the mean brightness of the pixels around a pixel of interest. The difference between the obtained mean brightness and the brightness of the pixel of interest is then obtained to produce an output signal which emphasizes he points where the brightness in the image changes. In the prior art image scanners, digital filters are generally used to discriminate the textual and other such information of interest from the background information. However, such digital filtering systems are costly, requiring memories and many other expensive parts and components. Additionally, the limited operating speed of such expensive parts and components makes it difficult to increase the speed of such prior art image scanners.